Ensemble Learning for Alcoholism Classification Using EEG Signals

Excessive drinking is a major risk factor that leads to many health complications. The diagnosis of alcoholism is challenging, especially when the standard diagnostic tests rely on blood tests and questionnaires that are subjective to the patient and the examiner. The study's major goal is to find new electroencephalography (EEG) classification methods to improve past findings and construct a robust EEG classification algorithm to generate accurate predictions with explainable results. The EEG records were examined from two different perspectives and combined with an ensemble of classification models. The first approach was temporal data, and the second was images derived from the original signals. Using fast Fourier transform (FFT) and independent component analysis (ICA), we convert 64-channel temporal data into images along with applying the Symbolic Aggregate approXimation (SAX) technique. Our model combines input data in tabular, temporal, and image formats with an ensemble of linear neural networks, long short-term memory (LSTM), and efficient-net classification models. We have evaluated our method using a publicly available dataset for EEG classification of alcoholic and nonalcoholic subjects. Overall, our algorithm's highest cross-validation classification accuracy is 85.52% compared to the state-of-the-art EEG-NET's accuracy of 81.19%.